[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119002-en":3,"doc-seo-119002-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119002,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",7,"Healthcare","Decoding children dental health risks - a machine learning approach to identifying key influencing factors","This study investigates key factors driving dental caries risk in children aged 7 and under using machine learning methods to support earlier identification and prevention of high-risk individuals. Clinical examination data from 356 children were analyzed with Logistic Regression, Decision Trees, and Random Forest models to evaluate influences of dietary habits, fluoride exposure, and socio-economic status. Results highlight poor oral hygiene, high sugary diet, and low fluoride exposure as significant risk factors, with Random Forest performing best, supported by SHAP interpretability and standard classification metrics.","University of Birmingham  \nDecoding children dental health risks  \nSadegh-Zadeh, Seyed-Ali; Bagheri, Mahshid; Saadat, Mozafar  \nDOI:  \n10.3389/frai.2024.1392597  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nSadegh-Zadeh, S-A, Bagheri, M & Saadat, M 2024, 'Decoding children dental health risks: a machine learning approach to identifying key influencing factors', Frontiers in Artificial Intelligence, vol. 7, 1392597. [https://doi.org/10.3389/frai.2024.1392597](https://doi.org/10.3389/frai.2024.1392597)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. 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Aug. 2026  \nTYPE Original Research PUBLISHED 17 June 2024  \nDOI 10.3389/frai.2024.1392597  \nOPEN ACCESS  \nEDITED BY  \nKezhi Li,  \nUniversity College London, United Kingdom  \nREVIEWED BY  \nChengzhe Piao,  \nUniversity College London, United Kingdom Pritika Bahad,  \nPrestige Institute of Engineering Management and Research, India  \n*CORRESPONDENCE  \nSeyed-Ali Sadegh-Zadeh  \n [ali.sadegh-zadeh@staffs.ac.uk](ali.sadegh-zadeh@staffs.ac.uk)[ ](ali.sadegh-zadeh@staffs.ac.uk)Mozafar Saadat  \n [m.saadat@bham.ac.uk](m.saadat@bham.ac.uk)[ ](m.saadat@bham.ac.uk)RECEIVED 27 February 2024 ACCEPTED 05 June 2024 PUBLISHED 17 June 2024  \nCITATION  \nSadegh-Zadeh S-A, Bagheri M and  \nSaadat M (2024) Decoding children dental health risks: a machine learning approach to identifying key influencing factors.  \nFront. Artif. Intell. 7:1392597.  \ndoi: 10.3389/frai.2024.1392597  \nCOPYRIGHT  \n© 2024 Sadegh-Zadeh, Bagheri and Saadat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDecoding children dental health risks: a machine learning approach to identifying key influencing factors  \nSeyed-Ali Sadegh-Zadeh 1*, Mahshid Bagheri 2 and Mozafar Saadat3*  \n1 Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stokeon-Trent, United Kingdom, 2 Paediatric Dentistry, Population and Patient Health, King’s College London Dental Institute, London, United Kingdom, 3 Department of Mechanical Engineering, School of Enginee","cbCaipkyE2WqVVW8","https://ap.wps.com/l/cbCaipkyE2WqVVW8","pdf",1695565,1,18,"English","en",105,"# Introduction and objectives\n# Methods\n# Results\n# Conclusion\n# Clinical significance\n# Keywords","[{\"question\":\"Which children’s age group and health outcome does the study focus on?\",\"answer\":\"The study focuses on dental caries risk in children aged 7 and under.\"},{\"question\":\"What data and machine learning models were used to assess risk factors?\",\"answer\":\"Clinical examinations of 356 children were analyzed using Logistic Regression, Decision Trees, and Random Forest models.\"},{\"question\":\"What factors were identified as significant dental caries risk drivers?\",\"answer\":\"Poor oral hygiene, high sugary diet, and low fluoride exposure were identified as significant risk factors, with SHAP analysis supporting their influence.\"}]","Decoding children dental health risks - 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